Classification of ECG beats using cross wavelet transform and support vector machines

Jacob, Neenu; Joseph, Liza Annie

doi:10.1109/raics.2015.7488412

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…The usage of wavelet-compressed data from the St. Petersburg Institute of Cardiological Technics 12lead arrhythmia database was our first step. In general, the MIT-BIH arrhythmia database is used by most research that is performed on ECG signals [1][2][3][4][5][6][7][8][9][10][11][12]14,16,19,[21][22][23]25,26], and classification is executed on a single channel [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][21][22][23][24][25][26][27]. Our research was performed on 12 channels.…”

Section: Discussionmentioning

confidence: 99%

“…Generally, neural networks are preferred for classification [1][2][3]6,11,12,19,25,27]. The SVM [4,5,8,14,16,18,21,22,24] is another popular technique for classification of arrhythmia. Random forest [9], linear classifier [10], morphology consistency evaluation [13], cluster and centroid identification [15], linear discriminant analysis [17], threshold based classifier [20], KNN [7,23], and naive Bayes [26] are the other classification methods that are used in related works.…”

Section: Discussionmentioning

confidence: 99%

“…They obtained 98.11% accuracy. Jacob and Joseph [22] performed classification using XWT and SVMs for two classes. The simulation resulted in an accuracy rate of 94.8% for the SVM and 96.2% for the two-dimensional SVM.…”

Section: Llamedo and Martínezmentioning

confidence: 99%

See 2 more Smart Citations

Bagged tree classi cation of arrhythmia using wavelets for denoising, compression, and feature extraction

Tomak¹,

Kayıkçıoğlu²

2018

Turk J Elec Eng & Comp Sci

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Arrhythmia, also known as dysrhythmia, is a condition involving an irregular heartbeat. A problem in the heart may cause problems in other organs, and as time passes, this will lead to more severe problems. Arrhythmia must be detected at an early stage to prevent such a problem occurring in the heart. Detection of arrhythmia from an electrocardiogram is an easy method that does not need much equipment and does not harm the patient. The purpose of this research is to find a faster and more accurate system to classify nine classes of arrhythmia. The St. Petersburg Institute of Cardiological Technics 12-lead arrhythmia database was used for training and testing. Data were compressed and preprocessed (denoising, trend elimination, baseline correction, and normalization) before being sent to the system for feature calculation. The wavelet coefficients that displayed the most significant effect on classification were chosen and used as features. Standard deviation and variance were also added to the feature set. Later, principal component analysis (PCA) was used to reduce the number of features further. After deciding the features, the performance of the basic classification methods and spiking neural network was checked to determine whether there was a better classifier to be used for our research. Tenfold cross-validation was applied to the training dataset. Bagged trees were found to produce better results. The classifiers' performance was tested by sensitivity, specificity, and accuracy.

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